58,047 research outputs found
Guided Dropout
Dropout is often used in deep neural networks to prevent over-fitting.
Conventionally, dropout training invokes \textit{random drop} of nodes from the
hidden layers of a Neural Network. It is our hypothesis that a guided selection
of nodes for intelligent dropout can lead to better generalization as compared
to the traditional dropout. In this research, we propose "guided dropout" for
training deep neural network which drop nodes by measuring the strength of each
node. We also demonstrate that conventional dropout is a specific case of the
proposed guided dropout. Experimental evaluation on multiple datasets including
MNIST, CIFAR10, CIFAR100, SVHN, and Tiny ImageNet demonstrate the efficacy of
the proposed guided dropout.Comment: Accepted in AAAI201
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
We propose a guided dropout regularizer for deep networks based on the
evidence of a network prediction defined as the firing of neurons in specific
paths. In this work, we utilize the evidence at each neuron to determine the
probability of dropout, rather than dropping out neurons uniformly at random as
in standard dropout. In essence, we dropout with higher probability those
neurons which contribute more to decision making at training time. This
approach penalizes high saliency neurons that are most relevant for model
prediction, i.e. those having stronger evidence. By dropping such high-saliency
neurons, the network is forced to learn alternative paths in order to maintain
loss minimization, resulting in a plasticity-like behavior, a characteristic of
human brains too. We demonstrate better generalization ability, an increased
utilization of network neurons, and a higher resilience to network compression
using several metrics over four image/video recognition benchmarks
Predictors of treatment dropout in self-guided web-based interventions for depression: an ‘individual patient data’ meta-analysis
Background. It is well known that web-based interventions can be effective treatments for depression. However, dropout
rates in web-based interventions are typically high, especially in self-guided web-based interventions. Rigorous empirical
evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small
study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to
gain a better understanding of who may benefit from these interventions.
Method. A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with
depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the
selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined.
Results. Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed.
The multivariate analysis indicated that male gender [relative risk (RR) 1.08], lower educational level (primary
education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while
for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94).
Conclusions. Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform
tailoring of online self-help interventions to prevent dropout in identified groups at ris
Psychotherapy dropout : the influence of ethnic identity and stigma on early termination.
Although clients continue to drop out of psychotherapy, researchers have made few inroads into understanding the dropout phenomenon. Clinical studies have reported client dropout rates based on demographic factors (e.g., race, socioeconomic status, educational status). However, few studies have investigated the cultural correlates that may underlie these demographic factors and affect client termination. Furthermore, none have provided an empirically driven explanation as to why some clients drop out more often than others. In two studies, I explore dropout in two settings: a community mental health center and a university counseling center. Guided by current theory and research, I will examine the association between clients’ ethnic identity, the stigma they experience, and their decision to terminate therapy. Through logistic regression, I will seek to explore these cultural influences on the dropout process. It is expected that the study will contribute to current research and assist practitioners in preventing dropout
Guided Proofreading of Automatic Segmentations for Connectomics
Automatic cell image segmentation methods in connectomics produce merge and
split errors, which require correction through proofreading. Previous research
has identified the visual search for these errors as the bottleneck in
interactive proofreading. To aid error correction, we develop two classifiers
that automatically recommend candidate merges and splits to the user. These
classifiers use a convolutional neural network (CNN) that has been trained with
errors in automatic segmentations against expert-labeled ground truth. Our
classifiers detect potentially-erroneous regions by considering a large context
region around a segmentation boundary. Corrections can then be performed by a
user with yes/no decisions, which reduces variation of information 7.5x faster
than previous proofreading methods. We also present a fully-automatic mode that
uses a probability threshold to make merge/split decisions. Extensive
experiments using the automatic approach and comparing performance of novice
and expert users demonstrate that our method performs favorably against
state-of-the-art proofreading methods on different connectomics datasets.Comment: Supplemental material available at
http://rhoana.org/guidedproofreading/supplemental.pd
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